Manipulation of deformable objects is a desired skill in making robots ubiquitous in manufacturing, service, healthcare, and security. Deformable objects are common in our daily lives, e.g., wires, clothes, bed sheets, etc., and are significantly more difficult to model than rigid objects. In this study, we investigate vision-based manipulation of linear flexible objects such as cables. We propose a geometric modeling method that is based on visual feedback to develop a general representation of the linear flexible object that is subject to gravity. The model characterizes the shape of the object by combining the curvatures on two projection planes. In this approach, we achieve tracking of the position and orientation (pose) of a cable-like object, the pose of its tip, and the pose of the selected grasp point on the object, which enables closed-loop manipulation of the object. We demonstrate the feasibility of our approach by completing the Plug Task used in the 2015 DARPA Robotics Challenge Finals, which involves unplugging a power cable from one socket and plugging it into another. Experiments show that we can successfully complete the task autonomously within 30 seconds.
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Model-Based Manipulation of Linear Flexible Objects: Task Automation in Simulation and Real World
Manipulation of deformable objects is a desired skill in making robots ubiquitous in manufacturing, service, healthcare, and security. Common deformable objects (e.g., wires, clothes, bed sheets, etc.) are significantly more difficult to model than rigid objects. In this research, we contribute to the model-based manipulation of linear flexible objects such as cables. We propose a 3D geometric model of the linear flexible object that is subject to gravity and a physical model with multiple links connected by revolute joints and identified model parameters. These models enable task automation in manipulating linear flexible objects both in simulation and real world. To bridge the gap between simulation and real world and build a close-to-reality simulation of flexible objects, we propose a new strategy called Simulation-to-Real-to-Simulation (Sim2Real2Sim). We demonstrate the feasibility of our approach by completing the Plug Task used in the 2015 DARPA Robotics Challenge Finals both in simulation and real world, which involves unplugging a power cable from one socket and plugging it into another. Numerical experiments are implemented to validate our approach.
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- NSF-PAR ID:
- 10194659
- Date Published:
- Journal Name:
- Machines
- Volume:
- 8
- Issue:
- 3
- ISSN:
- 2075-1702
- Page Range / eLocation ID:
- 46
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This paper tackles the task of goal-conditioned dynamic manipulation of deformable objects. This task is highly challenging due to its complex dynamics (introduced by object deformation and high-speed action) and strict task requirements (defined by a precise goal specification). To address these challenges, we present Iterative Residual Policy (IRP), a general learning framework applicable to repeatable tasks with complex dynamics. IRP learns an implicit policy via delta dynamics—instead of modeling the entire dynamical system and inferring actions from that model, IRP learns delta dynamics that predict the effects of delta action on the previously observed trajectory. When combined with adaptive action sampling, the system can quickly optimize its actions online to reach a specified goal. We demonstrate the effectiveness of IRP on two tasks: whipping a rope to hit a target point and swinging a cloth to reach a target pose. Despite being trained only in simulation on a fixed robot setup, IRP is able to efficiently generalize to noisy real-world dynamics, new objects with unseen physical properties, and even different robot hardware embodiments, demonstrating its excellent generalization capability relative to alternative approaches.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/machines8030046
